Data Analysis: The Business Value of Parallel Research

It’s been well documented that companies are facing a shortage of skilled data scientists. In fact, the US faces a shortage of 140,000 to 190,000 with analytical skills and 1.5 million managers and analysts with the skills to make sense of big data and drive effective decision making, according to a study conducted by McKinsey Global Institute.

Despite the challenges that many organizations face in identifying and recruiting analytical talent, TechRepublic contributor John Weathington advocates doubling down on big data investments.

That includes creating two separate analytical teams to work on the same research questions to give senior management options for strategic planning.

There are several benefits for creating parallel analytical teams, says Weathington.

These benefits include dedicating more brainpower to problem solving, creating more diversity around the management and approaches to tackling specific issues, and stoking competition for delivering the best data analysis for senior management and the enterprise.

But the greatest advantage to developing parallel analytical teams, “is a sizeable decrease in your risk of failure (or an increase in your chance of success) from the quality of the analysis. This factor alone may bring your value proposition high enough to actually make it a better decision to double your [analytical] team,” Weathington adds.

As Weathington sees it, two separate analytical teams can run their own qualitative analyses to answer a number of “upfront unknowns, and since it’s an interpretive style of research, it lends to a fascinating aggregate discovery when both efforts are combined.”

So while it’s important for the two analytical teams to communicate with one another on the progress they’re each making toward resolving a particular business problem, they shouldn’t necessarily collaborate. The strength behind parallel research is that the quantitative analysis can be combined with the qualitative analysis from both teams and provide greater statistical rigor to the results.

As we’ve discussed previously, one of the greatest values that data scientists bring to the table is their ability to analyze a variety of different data streams from multiple perspectives, to spot underlying trends, and to recommend different ways to apply data.

By creating separate analytical teams – and not just merely adding more analytical talent to the same team – companies are able to expand the perspective being taken to tackle a particular business problem. They can also have distinct approaches to problem solving, as Weathington notes.

For organizations that are considering having two or more analytical teams, here’s some additional food for thought – be sure they’re not reporting into the lines of business requesting the analyses.

If an analytics team reports into the owner of a corporate function or business domain and the team’s rewards are aligned with the success of the projects being analyzed, the objectivity of the analysis could be dubious, according to Mukul Patki of Aryng. The team “could potentially introduce a bias to make the project/initiative look better than it actually is,” Patki notes.

Having two or more analytical teams working on the same projects with distinct reporting, separate from the corporate functions or business units they’re serving, can help strengthen objectivity and lead to more credible results.

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